The problem of motion-planning deals with generating a feasible trajectory, which connects an initial state of a system to a desired final state, while avoiding unsafe states, which represent physical constraints of the system as well as obstacles in the environment. Several motion-planning strategies are proposed in literature, which can be broadly classified into combinatorial strategies [1]}, [2]}, [3]} and sampling-based strategies [4]}, [5]}. The combinatorial strategies such as the potential field approach [1]}, the navigation function based approach [2]}, cell decomposition methods [3]}, etc. aim at utilizing the knowledge of the environment and to create a feasible path from an initial state to a final state either by utilizing a continuous model of the feasible environment or by systematically combining cells, obtained by decomposing the environment. On the other hand, the sampling-based strategies, most of which are based on the standard Rapidly exploring Random Tree (RRT) and RRT* algorithms [4]}, [5]}, aim at random explorations in the concerned state space and finding sample states, which can be feasibly connected. However, these algorithms are not inherently robust to the uncertainties which appear in practise due to various factors, such as incomplete knowledge of the model, unknown obstacles, exogenous disturbances, etc. It is essential for a motion-planning algorithm to generate robust trajectories which are feasible even under the effect of uncertainties, to ensure safety of the system as well as of the environment.
